发现活动及其时间意义

Ayesha Choudhary, T. Faruquie, Subhashis Banerjee, S. Chaudhury
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引用次数: 1

摘要

在本文中,我们讨论了在监视区域中发现活动及其时间意义的问题。发现活动及其在特定时间发生的预期在许多监视应用中起着重要作用。我们提出了一个无监督模型,称为时间pLSA模型,它扩展了概率潜在语义分析(pLSA)模型,以共同捕获活动及其随时间的行为。我们使用自适应背景减法来检测时空斑块,这些斑块被用作活动模式的特征表示。每个补丁都与它们发生的时隙相关联。使用多项分布来模拟活动在时空斑块上的分布和时间显著性在时间线上的分布。我们在户外场景的真实生活监控馈电中展示了我们的方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Discovering Activities and Their Temporal Significance
In this paper, we address the problem of discovering activities and their temporal significance in an area under surveillance. Discovering activities along with its expectation of occurrence at a particular time plays an important role in many surveillance applications. We propose an unsupervised model, called Time pLSA model, that extends the probabilistic Latent Semantic Analysis (pLSA) model to jointly capture the activities and their behaviour over time. We use adaptive background subtraction to detect spatio-temporal patches, which are used as feature representation for activity patterns. Each of these patches are associated with the time slot in which they occur. Multinomial distributions are used to model both activities as distribution over spatio-temporal patches and time significance as distribution over the time-line. We demonstrate the effectiveness of our approach on a real life surveillance feed of an outdoor scene.
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